# Incorporating Prior Knowledge in Deep Learning Models via Pathway Activity Autoencoders

arXiv: https://arxiv.org/abs/2306.05813 · HTML: https://mlsanity.com/paper/2306.05813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder](https://mlsanity.com/paper/2102.05892/) (2021)
- [Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders](https://mlsanity.com/paper/2206.10699/) (2022)

## Built on by (most cited first, 1 of 1)

- [Quantum Spectral Anomaly Detection](https://mlsanity.com/paper/2607.05307/) (2026)

## Abstract (arXiv)

Motivation: Despite advances in the computational analysis of high-throughput molecular profiling assays (e.g. transcriptomics), a dichotomy exists between methods that are simple and interpretable, and ones that are complex but with lower degree of interpretability. Furthermore, very few methods deal with trying to translate interpretability in biologically relevant terms, such as known pathway cascades. Biological pathways reflecting signalling events or metabolic conversions are Small improvements or modifications of existing algorithms will generally not be suitable, unless novel biological results have been predicted and verified. Determining which pathways are implicated in disease and incorporating such pathway data as prior knowledge may enhance predictive modelling and personalised strategies for diagnosis, treatment and prevention of disease. Results: We propose a novel prior-knowledge-based deep auto-encoding framework, PAAE, together with its accompanying generative variant, PAVAE, for RNA-seq data in cancer. Through comprehensive comparisons among various learning models, we show that, despite having access to a smaller set of features, our PAAE and PAVAE models achieve better out-of-set reconstruction results compared to common methodologies. Furthermore, we compare our model with equivalent baselines on a classification task and show that they achieve better results than models which have access to the full input gene set. Another result is that using vanilla variational frameworks might negatively impact both reconstruction outputs as well as classification performance. Finally, our work directly contributes by providing comprehensive interpretability analyses on our models on top of improving prognostication for translational medicine.